In [1]:
import requests
from bs4 import BeautifulSoup
from pytickersymbols import PyTickerSymbols
import numpy as np
import pandas as pd
import yfinance as yf
import datetime as dt
import copy
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px
In [2]:
#Treemap generation for NASDAQ 100

from warnings import simplefilter
simplefilter(action="ignore", category=pd.errors.PerformanceWarning)


stock_data = PyTickerSymbols()
Nasdaq100_yahoo = stock_data.get_nasdaq_100_nyc_yahoo_tickers()

#Nasdaq100_yahoo = ["AAPL","MSFT"]
df=pd.DataFrame(np.nan,index=['recommendationKey','industry','beta','marketCap','Symbols'],
               columns=Nasdaq100_yahoo)



for ticker in Nasdaq100_yahoo:
    try:
        df[ticker].iloc[0]=yf.Ticker(ticker).info['recommendationKey']
        df[ticker].iloc[1]=yf.Ticker(ticker).info['industry']
        df[ticker].iloc[2]=yf.Ticker(ticker).info['beta']
        df[ticker].iloc[3]=yf.Ticker(ticker).info['marketCap']
        df[ticker].iloc[4]=ticker
    except:
        print("Some value is missing")
    
#yf.Ticker('TCOM').info['recommendationKey']

data=yf.download(Nasdaq100_yahoo,period='1y',interval='1d')['Adj Close']
data.head()

df_T=df.T


return_df = pd.DataFrame()
return_df = (data.iloc[-1]/data.iloc[0])-1
return_df.name = "YTD_Performance"

return_df_df = pd.DataFrame(return_df)

final2=df_T.join(return_df_df)

C:\Users\toram\anaconda3\envs\quant\lib\site-packages\pandas\core\indexing.py:1732: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_block(indexer, value, name)

Some value is missing
Some value is missing
[*********************100%***********************]  108 of 108 completed

1 Failed download:
- CTRP: No data found, symbol may be delisted
In [3]:
final2.dropna(inplace=True,how="all")
In [4]:
# fillna() on multiple columns with different values
final3 =  final2.fillna(value={'recommendationKey':'none','industry':'Trading-Nasdaq','beta':1,'marketCap':1000000000,'Symbols':'AAA','YTD_Performance':0})
In [5]:
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
final3.head(100)
Out[5]:
recommendationKey industry beta marketCap Symbols YTD_Performance
AAPL buy Consumer Electronics 1.203116 2935843454976 AAPL 0.479000
CSCO buy Communication Equipment 0.954232 231258914816 CSCO 0.087779
INTC hold Semiconductors 0.545767 212497317888 INTC -0.170928
MSFT buy Software—Infrastructure 0.910925 2365224386560 MSFT 0.348380
WBA hold Pharmaceutical Retailers 0.558070 41195339776 WBA -0.061815
ATVI buy Electronic Gaming & Multimedia 0.544291 62728228864 ATVI -0.138745
ADBE buy Software—Infrastructure 1.054625 219890728960 ADBE -0.007777
GOOGL buy Internet Content & Information 1.064960 1888858931200 GOOGL 0.397618
GOOG strong_buy Internet Content & Information 1.064960 1898159800320 GOOG 0.396560
AMZN buy Internet Retail 1.114665 1730196799488 AMZN 0.099209
AMGN hold Drug Manufacturers—General 0.588582 134188277760 AMGN -0.024430
ADI buy Semiconductors 1.098425 89285394432 ADI 0.125186
AMAT buy Semiconductor Equipment & Materials 1.411909 125181493248 AMAT 0.130697
ADSK buy Software—Application 1.459153 49086971904 ADSK -0.178374
ADP hold Staffing & Employment Services 0.833169 94333706240 ADP 0.191385
BIDU buy Internet Content & Information 0.953740 53738979328 BIDU -0.280459
BAIDF none Internet Content & Information 0.953740 55151149056 BAIDF 0.000000
BIIB buy Drug Manufacturers—General 0.447342 31517687808 BIIB -0.223395
CERN hold Health Information Services 0.813484 27456997376 CERN 0.310028
CHTR buy Entertainment 0.960137 109675692032 CHTR -0.101919
CHKP hold Software—Infrastructure 0.625492 18498762752 CHKP 0.201337
CTSH buy Information Technology Services 1.103838 48703074304 CTSH 0.202892
CMCSA buy Entertainment 0.937007 216280399872 CMCSA -0.118628
COST buy Discount Stores 0.661909 252225486848 COST 0.609125
CSX buy Railroads 1.220472 81990852608 CSX 0.181643
TCOM strong_buy Travel Services 1.040354 18304057344 TCOM -0.364851
DLTR buy Discount Stores 0.734744 35805995008 DLTR 0.367343
EBAY buy Internet Retail 1.153051 35369246720 EBAY 0.016766
EA buy Electronic Gaming & Multimedia 0.835137 36190461952 EA -0.043245
FB buy Internet Content & Information 1.387303 627829047296 FB -0.210525
FAST hold Industrial Distribution 1.281496 33946173440 FAST 0.186823
FISV buy Information Technology Services 0.812992 67456733184 FISV -0.150671
GILD buy Drug Manufacturers—General 0.424212 75899944960 GILD -0.046185
ILMN buy Diagnostics & Research 0.923228 56116510720 ILMN -0.097316
INCY buy Biotechnology 0.692421 17661734912 INCY -0.017756
INTU buy Software—Application 1.132381 137563996160 INTU 0.303246
ISRG buy Medical Instruments & Supplies 1.169783 108318842880 ISRG 0.250432
JD buy Internet Retail 0.693897 97889099776 JD -0.234100
JDCMF none Internet Retail 0.693897 94445412352 JDCMF -0.225115
KHC hold Packaged Foods 0.988188 48168800256 KHC 0.006445
LRCX buy Semiconductor Equipment & Materials 1.261318 79488491520 LRCX 0.006710
MAR buy Lodging 1.687500 58514653184 MAR 0.235072
MCHP buy Semiconductors 1.562007 44407001088 MCHP 0.068737
MU buy Semiconductors 1.169291 92157894656 MU -0.051021
MDLZ buy Confectioners 0.724409 87339835392 MDLZ 0.069031
MNST buy Beverages—Non-Alcoholic 1.149114 43719757824 MNST -0.102752
NTES buy Electronic Gaming & Multimedia 0.510826 63599157248 NTES -0.081108
NETTF none Electronic Gaming & Multimedia 0.510826 64182345728 NETTF -0.111571
NFLX buy Entertainment 0.986220 176755507200 NFLX -0.239080
NVDA buy Semiconductors 1.411417 715752472576 NVDA 1.207283
NXPI buy Semiconductors 1.408956 51103019008 NXPI 0.011170
ORLY buy Specialty Retail 1.020177 46835081216 ORLY 0.379820
PCAR buy Farm & Heavy Construction Machinery 0.939960 31560200192 PCAR 0.005921
PAYX hold Staffing & Employment Services 0.964566 47764221952 PAYX 0.352906
PYPL buy Credit Services 1.292322 144168517632 PYPL -0.486316
QCOM buy Semiconductors 1.258366 181086355456 QCOM 0.244974
REGN buy Biotechnology 0.184547 75563220992 REGN 0.448351
ROST buy Apparel Retail 1.082677 33806612480 ROST -0.195642
SIRI buy Broadcasting 0.955708 26174777344 SIRI 0.124519
SWKS buy Semiconductors 1.106791 22636365824 SWKS -0.210886
SBUX buy Restaurants 0.901082 104966717440 SBUX -0.151522
TMUS buy Telecom Services 0.502460 160383860736 TMUS 0.038717
TSLA buy Auto Manufacturers 2.048720 1141188853760 TSLA 0.794337
TXN buy Semiconductors 0.934055 176314368000 TXN 0.050632
VRSK buy Consulting Services 0.846948 34675040256 VRSK 0.199504
VRTX buy Biotechnology 0.573818 65342279680 VRTX 0.185686
BKNG buy Travel Services 1.207677 97835712512 BKNG 0.007077
EXC buy Utilities—Diversified 0.570374 45924319232 EXC 0.566310
HON buy Conglomerates 1.151574 134941745152 HON -0.083414
AMD buy Semiconductors 1.848425 200328019968 AMD 0.591392
ALGN buy Medical Devices 1.640255 35637628928 ALGN -0.121318
AEP buy Utilities—Regulated Electric 0.335629 49713016832 AEP 0.190503
ANSS hold Software—Application 1.273129 28622780416 ANSS -0.043187
AVGO buy Semiconductors 1.019192 261405999104 AVGO 0.392361
CDNS buy Software—Application 1.123031 46257737728 CDNS 0.249266
CTAS buy Specialty Business Services 1.497047 44323598336 CTAS 0.276973
CPRT buy Auto & Truck Dealerships 1.127460 31024232448 CPRT 0.229457
FOXA buy Broadcasting 0.961391 22081841152 FOXA 0.104774
FXBY none Asset Management 0.984251 8034426 FXBY 0.000000
FOX none Broadcasting 0.961391 22099783680 FOX 0.058128
IDXX buy Diagnostics & Research 1.002952 46468014080 IDXX 0.149130
KLAC buy Semiconductor Equipment & Materials 1.256397 57821810688 KLAC 0.232761
SNPS buy Software—Infrastructure 1.166338 52300148736 SNPS 0.416930
VRSN buy Software—Infrastructure 0.911909 24389871616 VRSN 0.099066
ASMLF none Semiconductor Equipment & Materials 0.981876 289445806080 ASMLF 0.164773
ASML buy Semiconductor Equipment & Materials 0.981876 286221828096 ASML 0.173037
CDW buy Information Technology Services 1.115157 24412043264 CDW 0.104515
DXCM strong_buy Medical Devices 0.889763 49986842624 DXCM 0.473824
PEP buy Beverages—Non-Alcoholic 0.653051 232316846080 PEP 0.192440
XEL buy Utilities—Regulated Electric 0.354822 39047356416 XEL 0.101230
TEAM buy Software—Application 0.950295 79243837440 TEAM 0.526297
DOCU buy Software—Application 0.924769 22081935360 DOCU -0.438576
KDP buy Beverages—Non-Alcoholic 0.610985 54180806656 KDP 0.099378
LULU buy Apparel Retail 1.342027 44205764608 LULU 0.090002
MRVL buy Semiconductors 1.197834 64504860672 MRVL 0.638081
MTCH buy Internet Content & Information 1.000000 31736973312 MTCH -0.166053
MELI buy Internet Retail 1.511318 63599538176 MELI -0.104349
MRNA buy Biotechnology 1.546391 72807579648 MRNA 0.462405
OKTA buy Software—Infrastructure 1.080952 24593436672 OKTA -0.274210
PTON buy Leisure 0.997116 10355867648 PTON -0.703771
In [6]:
fig = px.treemap(final3.loc[final3["beta"].ge(0)],path=[px.Constant("NASDAQ100-Index"),'Symbols'],values="beta",color="YTD_Performance")
fig.show()
In [7]:
fig = px.treemap(final3.loc[final3["beta"].ge(0)],path=[px.Constant("NASDAQ100-Index-by-Beta-Recommendations as per Yahoofinance"),'recommendationKey','industry','Symbols'],values="beta",color="YTD_Performance")
fig.show()
In [8]:
fig = px.treemap(final3.loc[final3["beta"].ge(0)],path=[px.Constant("NASDAQ100-Index-by-MarketCap"),'recommendationKey','industry','Symbols'],values="marketCap",color="YTD_Performance")
fig.show()
In [9]:
#Stock screening for Most actively traded stocks in NASDAQ. 
url_volume = ("https://www.marketwatch.com/tools/screener/market?exchange=nasdaq&subreport=mostactive")
page = requests.get(url_volume)
soup_volume = BeautifulSoup(page.text, 'lxml')
volume = soup_volume.findAll('div', class_= 'cell__content fixed--cell')
most_active_tickers = []
for div_tag in soup_volume.findAll('div', class_= 'cell__content fixed--cell'):
     most_active_tickers.append(div_tag.text) 
most_active_tickers.pop(0)     
most_active_tickers
Out[9]:
['HYMC',
 'SXTC',
 'SNDL',
 'CLVR',
 'HOOD',
 'TQQQ',
 'AMD',
 'SQQQ',
 'TLRY',
 'AAPL',
 'GFAI',
 'MULN',
 'SOFI',
 'QQQ',
 'VEON',
 'AAL',
 'NVDA',
 'IGMS',
 'BBIG',
 'FAMI',
 'LCID',
 'MU',
 'IMPP',
 'FB',
 'MVIS']
In [10]:
ma_df=pd.DataFrame(np.nan,index=['recommendationKey','industry','beta','marketCap','Symbols','averageVolume'],
               columns=most_active_tickers)

for ticker in most_active_tickers:
    try:
        ma_df[ticker].iloc[0]=yf.Ticker(ticker).info['recommendationKey']
        ma_df[ticker].iloc[1]=yf.Ticker(ticker).info['industry']
        ma_df[ticker].iloc[2]=yf.Ticker(ticker).info['beta']
        ma_df[ticker].iloc[3]=yf.Ticker(ticker).info['marketCap']
        ma_df[ticker].iloc[4]=ticker
        ma_df[ticker].iloc[5]=yf.Ticker(ticker).info['averageVolume']
    except:
        print("Some value is missing")
    

ma_data=yf.download(most_active_tickers,period='1y',interval='1d')['Adj Close']
ma_data.head()

ma_df_T=ma_df.T


ma_return_df = pd.DataFrame()
ma_return_df = (ma_data.iloc[-1]/ma_data.iloc[0])-1
ma_return_df.name = "MostActive_YTD_Performance"

ma_return_df_df = pd.DataFrame(ma_return_df)

ma_final2=ma_df_T.join(ma_return_df_df)

C:\Users\toram\anaconda3\envs\quant\lib\site-packages\pandas\core\indexing.py:1732: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


Some value is missing
Some value is missing
Some value is missing
[*********************100%***********************]  25 of 25 completed
In [11]:
ma_final2.dropna(inplace=True,how="all")
ma_final2
Out[11]:
recommendationKey industry beta marketCap Symbols averageVolume MostActive_YTD_Performance
HYMC buy Gold 0.321078 279922496 HYMC 35092585 -0.264205
SXTC none Drug Manufacturers—Specialty & Generic 0.240225 8950218 SXTC 6601649 -0.842097
SNDL hold Drug Manufacturers—Specialty & Generic 5.238538 1516548352 SNDL 91795298 -0.363947
CLVR buy Drug Manufacturers—Specialty & Generic 0.510278 84192112 CLVR 3362934 -0.772302
HOOD hold Software—Infrastructure None 13316482048 HOOD 22483909 NaN
TQQQ NaN NaN NaN NaN NaN NaN 0.432791
AMD buy Semiconductors 1.848425 199595704320 AMD 100743022 0.621447
SQQQ NaN NaN NaN NaN NaN NaN -0.543709
TLRY hold Drug Manufacturers—Specialty & Generic None 4002027264 TLRY 28974606 -0.622505
AAPL buy Consumer Electronics 1.185531 2918724665344 AAPL 94158368 0.501448
GFAI buy Security & Protection Services None 41207344 GFAI 10721878 NaN
MULN none Auto Manufacturers None 81240848 MULN 102878332 -0.780784
SOFI buy Credit Services None 8104250368 SOFI 64742709 -0.385455
QQQ NaN NaN NaN NaN NaN NaN 0.186588
VEON buy Telecom Services 1.557578 1834878464 VEON 32301342 -0.592959
AAL hold Airlines 1.595472 11785500672 AAL 44455550 -0.247098
NVDA buy Semiconductors 1.411417 713534603264 NVDA 52568696 1.227826
IGMS buy Biotechnology -0.841384 946258560 IGMS 312481 -0.607114
BBIG none Leisure 0.303172 624756992 BBIG 39473619 0.325490
FAMI none Packaged Foods -0.174217 96661016 FAMI 32349147 -0.848972
LCID buy Auto Manufacturers None 45150531584 LCID 33635178 0.163760
MU buy Semiconductors 1.169291 91827552256 MU 22220868 -0.049546
IMPP none Oil & Gas E&P None 92710928 IMPP 37928904 NaN
FB buy Internet Content & Information 1.387303 625528995840 FB 36568319 -0.201875
MVIS hold Scientific & Technical Instruments 3.423228 913696512 MVIS 5551718 -0.549147
In [12]:
# fillna() on multiple columns with different values
ma_final3 =  ma_final2.fillna(value={'recommendationKey':'none','industry':'Trading-Nasdaq','beta':1,'marketCap':1000000000,'Symbols':'AAA','MostActive_YTD_Performance':0.01,'averageVolume':10000000})
ma_final3
Out[12]:
recommendationKey industry beta marketCap Symbols averageVolume MostActive_YTD_Performance
HYMC buy Gold 0.321078 279922496 HYMC 35092585 -0.264205
SXTC none Drug Manufacturers—Specialty & Generic 0.240225 8950218 SXTC 6601649 -0.842097
SNDL hold Drug Manufacturers—Specialty & Generic 5.238538 1516548352 SNDL 91795298 -0.363947
CLVR buy Drug Manufacturers—Specialty & Generic 0.510278 84192112 CLVR 3362934 -0.772302
HOOD hold Software—Infrastructure 1.000000 13316482048 HOOD 22483909 0.010000
TQQQ none Trading-Nasdaq 1.000000 1000000000 AAA 10000000 0.432791
AMD buy Semiconductors 1.848425 199595704320 AMD 100743022 0.621447
SQQQ none Trading-Nasdaq 1.000000 1000000000 AAA 10000000 -0.543709
TLRY hold Drug Manufacturers—Specialty & Generic 1.000000 4002027264 TLRY 28974606 -0.622505
AAPL buy Consumer Electronics 1.185531 2918724665344 AAPL 94158368 0.501448
GFAI buy Security & Protection Services 1.000000 41207344 GFAI 10721878 0.010000
MULN none Auto Manufacturers 1.000000 81240848 MULN 102878332 -0.780784
SOFI buy Credit Services 1.000000 8104250368 SOFI 64742709 -0.385455
QQQ none Trading-Nasdaq 1.000000 1000000000 AAA 10000000 0.186588
VEON buy Telecom Services 1.557578 1834878464 VEON 32301342 -0.592959
AAL hold Airlines 1.595472 11785500672 AAL 44455550 -0.247098
NVDA buy Semiconductors 1.411417 713534603264 NVDA 52568696 1.227826
IGMS buy Biotechnology -0.841384 946258560 IGMS 312481 -0.607114
BBIG none Leisure 0.303172 624756992 BBIG 39473619 0.325490
FAMI none Packaged Foods -0.174217 96661016 FAMI 32349147 -0.848972
LCID buy Auto Manufacturers 1.000000 45150531584 LCID 33635178 0.163760
MU buy Semiconductors 1.169291 91827552256 MU 22220868 -0.049546
IMPP none Oil & Gas E&P 1.000000 92710928 IMPP 37928904 0.010000
FB buy Internet Content & Information 1.387303 625528995840 FB 36568319 -0.201875
MVIS hold Scientific & Technical Instruments 3.423228 913696512 MVIS 5551718 -0.549147
In [13]:
fig = px.treemap(ma_final3.loc[ma_final3["beta"].ge(0)],path=[px.Constant("Most-Active-NASDAQ-avgVolume-Recommendation-mentioned-in-Yahoo"),'recommendationKey','industry','Symbols'],values="averageVolume",color="MostActive_YTD_Performance")
fig.show()